AI Software User Research on Personal Financial Management of GenZ
Budgetwise.ai is an AI-powered personal finance app that simplifies money management for young users
Context: Product was going through a major update with AI technology
My Role: Lead UX researcher
Methods: Market exploration / Survey / In-depth interviews / Usability testing / Feature prioritization / Competitive Analysis
Goals
Product Goal
To revolutionize how young users approach personal finance by leveraging AI-powered technology.
Research Goal:
Find the market gaps that could make this app stand out
Understand what users truly need, what frustrates them, and how they manage their money with budget plans
Challenges
When I first joined the team, the startup team faced several significant hurdles:
01
Navigating a lack of clear product direction during a redesign phase + unplanned leave of PM lead
02
Competition from big tech companies in the crowded AI market
03
Operating within a constrained budget
04
A team inexperienced in AI product development
Research Solution
To tackle the challenges effectively, I designed a multi-phased, mixed-method research approach, specifically ordered to address specific questions and provide actionable insights:
I summarized the framework tailored for this problem space, named “4C” (i.e., Clarify, Collect, Connect, and Collaborate)
Step 1: Clarify – Survey
At the beginning of the project, our stakeholders needed clarity on the market size and target user segments.
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Provide quantitative data to validate pricing models and identify which user segments were most interested in an AI-powered finance tool
Serve as a cost-effective method, allowing us to collect over 800 responses within five days, which was crucial given our limited budget of $500
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What are the primary pain points for potential users in managing their finances?
Which features and functionalities are most desirable in a personal finance app?
How can AI capabilities enhance the user experience?
Based on survey findings, I identified two key user groups by analyzing budgeting habits, frustrations, and willingness to adopt new tools:
Current Budgeters: Use budgeting apps but find them frustrating due to usability issues or missing features
Potential Budgeters: Interested in budgeting but haven’t developed the habit due to complexity or lack of motivation
Step 2: Collect – In-Depth Interviews
The PM and stakeholders needed research to tackle an important problem space in the budgeting app ecosystem.
They wanted to explore better ways regarding monthly budgeting habits and automation features, this required me to apply psychological principles.
Surveys provided useful information but lacked details on income, AI views, and community needs. We recognized the need for in-depth interviews to explore:
Why do competitor apps focus on fresh monthly budgets instead of replicating previous ones?
Would an automated budget based on past spending habits truly benefit users?
Are budgets transferable or do users prefer starting fresh each month?
What communication style do users want from an AI assistant?
10 participants (5 per persona) were recruited online through UserInterview to ensure a diverse range of experiences and financial literacy levels.
INITIAL RESULTS
Following the survey and user interview, the results showed that
Survey insights convinced stakeholders to pivot toward an AI-driven strategy, realigning the product’s roadmap and strategy
However, outcomes from interviews showed that users were cautiously optimistic about AI assistants but wanted transparency and control over suggestions (e.g., tailored financial advice, automated categorization, and predictive insights)
Interesting validation about the community feature — seen as useful for shared tips, accountability, and learning from others
THE POT TWIST
As the team gained momentum, unforeseen challenges arose…
Early user engagement showed a different story: people weren’t using AI-driven budgeting tools as expected.
Some didn’t trust it, while others felt it didn’t adapt to their needs; they were confused by how the AI assistant communicated
Misaligned expectations from stakeholders required revisiting the product's design and strategy
These new challenges pushed me to rethink our approach
-> Here’s how we turned things around:
Instead of assuming AI was the problem, I applied Human-Centered AI (HCAI) principles to understand how users perceived, interacted with and relied on AI-generated financial recommendations.
Step 3: Refine – Researching AI Trust & Adoption
The new problem wasn’t just about what the AI was doing, but how it was doing it.
If AI was going to be the core of the product, it needed to earn users’ trust and feel like a real financial assistant—not just an automated script.
So, I shifted focus to understanding why users were hesitant about AI-driven budgeting and what would make them trust it.
✓ Understanding the AI Adoption Barriers
User Interviews – I continued the user interview to the 2nd round to further explore how users currently manage money and whether they trust AI for financial decisions
Behavioral Observation – During the remote interview, I also tracked where users dropped off when interacting with AI-generated suggestions
What I found:
Lack of transparency – Users didn’t understand why AI-generated budgets and alerts were made, leading to distrust
Unclear value proposition – Users weren’t sure how AI actually helped them beyond what they could already do manually
✓ Designing AI to Work With Users, Not For Them
Instead of replacing human decision-making, AI needed to act as a collaborative tool.
The goal was simple—let users feel in control of AI, not the other way around.
I recommended three key changes in the following development process:
✧ AI Confidence Scores – A transparency layer that explains why AI makes budgeting suggestions, similar to explainable AI (XAI) in fintech
✧ Personalization Controls – Users could adjust automation settings, choosing between fully AI-driven, hybrid, or manual budgeting
Step 3: Connect – Competitive Analysis
Before prioritizing features, we needed to understand how competitors positioned themselves in the market.
Key Questions Addressed:
What are the gaps in competitor products that our product can address?
How do competitors implement AI features, and what can we do differently?
Which aspects of competitors’ user experience resonate most with our targeted users?
Here is my process:
Identify opportunities for differentiation by analyzing strengths and weaknesses in competitors like Wally and Cleo
Use thematic analysis of competitor reviews to pinpoint areas for improvement in our own product design
How did I deliver the findings to the designers?
Utilized screenshots to visualize concepts and facilitate designers' comprehension
Regular workshops and check-ins ensured everyone stayed aligned at critical decision points, and invested in the process
Proactively engaged team members in research sessions and shared interim insights via 1 on 1 to maintain alignment
How did I deliver findings to the designers?
> Utilized screenshots to visualize concepts and facilitate designers' comprehension
Evaluation Process:
Analyzed Wally & Cleo to determine their strengths and weaknesses
Conducted a side-by-side comparison of our prototype's design on AI Chat feature against competitors
Extracted insights and areas of improvement for AI features for a better user experience
With insights from surveys, interviews, and competitive analysis, we found that “Features and Functionality” and “Ease of Use” were the top factors influencing users to switch tools.
Decision Factors to Switch Tools:
Step 4: Collaborate – Feature Prioritization Workshop
With insights from surveys, interviews, and competitive analysis, we found that “Features and Functionality” and “Ease of Use” were ranked as the biggest factors when deciding to switch tools.
Thus, we organized a collaborative workshop to define the Minimum Viable Product (MVP). The goal was to:
Align the team by prioritizing features that directly address user pain points and business objectives
Evaluate technical feasibility and resource limitations to pinpoint must-have features for the MVP while deferring less critical ones
My Approach:
I divided the workshop into 5 stages:
01. Introduction:
Reviewing user insights and establishing the purpose
03. Evaluation and Prioritization:
Ranking features based on criteria (e.g., user impact and feasibility)
05. Closing:
Finalizing timelines and check-ins with the engineering team
02. Idea Generation and Grouping:
Brainstorming features based on user feedback
04. Next Steps: Summarizing decisions and defining action plans
Step 5: Communicate — Insights and User Flow
After prioritizing key features, we designed a user flow to
Visualize how users interact with spending trends, category tracking, and Favorites
Help stakeholders understand the user journey easily, This promotes clear communication between teams, saving valuable time and resources. Increased efficiency. User flows eliminate guesswork and streamline the design process
Promote clear communication between teams, saving valuable time and resources, making development more efficient
Final Recommendation
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Introduce AI Confidence Scores which is clear, user-friendly explanation of why AI suggests certain budgets or spending insights
Display explainability tags (e g "This budget is based on your last 3 months of spending trends") to make AI reasoning more accessible
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Allow users to toggle AI involvement, and provide options for fully automated, hybrid, or manual budgeting modes
Implement AI learning preferences, letting users adjust how often AI suggests changes to their budget
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Add a “Teach AI” feature where users can provide quick feedback (e g “This suggestion isn’t relevant to me” or “Not helpful”)
Allow users to customize categories for AI-generated budgets, so it adapts to their unique financial habits over time
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Frame AI as an advisor, not a decision-maker—marketing and UX copy should emphasize “smart suggestions” instead of rigid automation
Provide AI insights with human explanations, such as “This recommendation is based on past spending, but you can adjust it as needed”
Final Outcomes & Impact:
Research efforts finally turned out:
User trust in AI-driven budgeting increased, leading to higher engagement with automated financial tools
Stakeholders shifted from treating AI as a secondary feature to making it a core strategy
The feedback loop improved AI accuracy; Consequently, recommendations became more relevant over time
What I will do differently next time?
Even though the refinement was very quick, I wish to test & measure AI effectiveness
The next time, once the proposed refinements were implemented, I would run usability tests to measure their impact.
A/B Testing: Engagement rates before and after adding transparency and personalization features
Sentiment Analysis: Do the trust levels in AI recommendations before and after design change?
Adoption Tracking: How many users customized AI settings vs. relying on default automation?
My Learnings:
Spend more time at the start aligning with stakeholders on goals, metrics, and expectations. I learned to create a dashboard for team goals and mark down what questions researchers are making efforts to solve with the team, which can reduce misaligned priorities later in the project
Incorporate higher-fidelity prototypes earlier in usability testing to capture deeper insights into user behavior and decision-making
Engage engineers earlier in the process to address technical feasibility and ensure research recommendations align with development capabilities
Develop scalable strategies to recruit a more diverse participant pool without exceeding budget constraints